扩展信息最大化盲源分离算法的研究

Research on blind source separation algorithm of the extension information maximization

盲源分离技术是信号处理和神经网络领域近年来的一个热点研究课题,由于其能够从观测的混合信号中恢复出源信号,而对源信号和混合系统的先验知识要求很少,因此在语音信号处理、无线信号处理、生物医学信号处理、地震信号处理,以及图像增强等方面都具有非常重要的理论意义和实用价值。信息最大化盲源分离算法能够有效地分离语音信号的瞬时混合,但是不能分离超高斯信号(如语音信号)和亚高斯信号(如正弦信号)的混合。基于此,本文讨论了扩展信启、最大化盲源分离算法,通过仿真表明,该算法可以有效的对各种源信号的线性即时混合进行分离,实验证明了该算法的有效性。

Blind Source Separation （BSS） is a new research field of the signal processing and Neural Network Because it can recover the source signals from the observed signals without any prior knowledge of the mixing system and source signals.So it has very important theory significance and utility value in audio signal processing,wireless signal processing,biomedical signal processing,earthquake signal processing,image enhancement and so on. Blind source separation algorithm of information maximization can effectively separates mixed instantaneous signals from voice signals, but it can not separates super-Gaussian signals （such as voice signals） and sub-Gaussian signals （such as sine signal） mix. Therefor, author proposes the blind source separation algorithm of information maximization , Experiment result shows that the algorithm can extract independent source from the hybrid mixture of any super-Gaussian and sub-Gaussian signals and displays a good calculation property .